Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Tuesday 30 June 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 161,896 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… 0-18 e380000… nhs_dar… 6 da121au South East
## [90m 2[39m 111 2020-03-18 fema… 0-18 e380000… nhs_don… 9 dn45hz North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_eas… 7 rh80bt South East
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_eas… 9 bn72pb South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_far… 8 po176ar South East
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_glo… 19 gl34fe South West
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_hal… 10 wa75td North West
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ham… 1 nw15jd London
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_har… 13 hg58qb North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_has… 6 tn402dz South East
## [90m# … with 161,886 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 64
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 101
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 12
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 5
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 0
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 7
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 2
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 4
## 116 2020-06-24 East of England 3
## 117 2020-06-25 East of England 0
## 118 2020-06-26 East of England 3
## 119 2020-06-27 East of England 1
## 120 2020-06-28 East of England 1
## 121 2020-03-01 London 0
## 122 2020-03-02 London 0
## 123 2020-03-03 London 0
## 124 2020-03-04 London 0
## 125 2020-03-05 London 0
## 126 2020-03-06 London 1
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## 137 2020-03-17 London 23
## 138 2020-03-18 London 27
## 139 2020-03-19 London 25
## 140 2020-03-20 London 44
## 141 2020-03-21 London 49
## 142 2020-03-22 London 54
## 143 2020-03-23 London 63
## 144 2020-03-24 London 87
## 145 2020-03-25 London 113
## 146 2020-03-26 London 129
## 147 2020-03-27 London 130
## 148 2020-03-28 London 122
## 149 2020-03-29 London 146
## 150 2020-03-30 London 149
## 151 2020-03-31 London 181
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## 153 2020-04-02 London 191
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## 174 2020-04-23 London 77
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## 241 2020-03-01 Midlands 0
## 242 2020-03-02 Midlands 0
## 243 2020-03-03 Midlands 1
## 244 2020-03-04 Midlands 0
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## 248 2020-03-08 Midlands 3
## 249 2020-03-09 Midlands 1
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## 251 2020-03-11 Midlands 2
## 252 2020-03-12 Midlands 6
## 253 2020-03-13 Midlands 5
## 254 2020-03-14 Midlands 4
## 255 2020-03-15 Midlands 5
## 256 2020-03-16 Midlands 11
## 257 2020-03-17 Midlands 8
## 258 2020-03-18 Midlands 13
## 259 2020-03-19 Midlands 8
## 260 2020-03-20 Midlands 28
## 261 2020-03-21 Midlands 13
## 262 2020-03-22 Midlands 31
## 263 2020-03-23 Midlands 33
## 264 2020-03-24 Midlands 41
## 265 2020-03-25 Midlands 48
## 266 2020-03-26 Midlands 64
## 267 2020-03-27 Midlands 72
## 268 2020-03-28 Midlands 89
## 269 2020-03-29 Midlands 92
## 270 2020-03-30 Midlands 90
## 271 2020-03-31 Midlands 123
## 272 2020-04-01 Midlands 140
## 273 2020-04-02 Midlands 142
## 274 2020-04-03 Midlands 124
## 275 2020-04-04 Midlands 151
## 276 2020-04-05 Midlands 164
## 277 2020-04-06 Midlands 140
## 278 2020-04-07 Midlands 123
## 279 2020-04-08 Midlands 186
## 280 2020-04-09 Midlands 139
## 281 2020-04-10 Midlands 127
## 282 2020-04-11 Midlands 142
## 283 2020-04-12 Midlands 139
## 284 2020-04-13 Midlands 120
## 285 2020-04-14 Midlands 116
## 286 2020-04-15 Midlands 147
## 287 2020-04-16 Midlands 102
## 288 2020-04-17 Midlands 118
## 289 2020-04-18 Midlands 115
## 290 2020-04-19 Midlands 92
## 291 2020-04-20 Midlands 107
## 292 2020-04-21 Midlands 86
## 293 2020-04-22 Midlands 78
## 294 2020-04-23 Midlands 103
## 295 2020-04-24 Midlands 79
## 296 2020-04-25 Midlands 72
## 297 2020-04-26 Midlands 81
## 298 2020-04-27 Midlands 74
## 299 2020-04-28 Midlands 68
## 300 2020-04-29 Midlands 53
## 301 2020-04-30 Midlands 56
## 302 2020-05-01 Midlands 64
## 303 2020-05-02 Midlands 51
## 304 2020-05-03 Midlands 52
## 305 2020-05-04 Midlands 61
## 306 2020-05-05 Midlands 59
## 307 2020-05-06 Midlands 59
## 308 2020-05-07 Midlands 48
## 309 2020-05-08 Midlands 34
## 310 2020-05-09 Midlands 37
## 311 2020-05-10 Midlands 42
## 312 2020-05-11 Midlands 33
## 313 2020-05-12 Midlands 45
## 314 2020-05-13 Midlands 40
## 315 2020-05-14 Midlands 37
## 316 2020-05-15 Midlands 40
## 317 2020-05-16 Midlands 34
## 318 2020-05-17 Midlands 31
## 319 2020-05-18 Midlands 34
## 320 2020-05-19 Midlands 34
## 321 2020-05-20 Midlands 36
## 322 2020-05-21 Midlands 32
## 323 2020-05-22 Midlands 27
## 324 2020-05-23 Midlands 34
## 325 2020-05-24 Midlands 19
## 326 2020-05-25 Midlands 26
## 327 2020-05-26 Midlands 33
## 328 2020-05-27 Midlands 29
## 329 2020-05-28 Midlands 28
## 330 2020-05-29 Midlands 20
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## 332 2020-05-31 Midlands 22
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## 334 2020-06-02 Midlands 22
## 335 2020-06-03 Midlands 24
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## 337 2020-06-05 Midlands 21
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## 342 2020-06-10 Midlands 15
## 343 2020-06-11 Midlands 13
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## 345 2020-06-13 Midlands 6
## 346 2020-06-14 Midlands 17
## 347 2020-06-15 Midlands 12
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## 350 2020-06-18 Midlands 14
## 351 2020-06-19 Midlands 9
## 352 2020-06-20 Midlands 13
## 353 2020-06-21 Midlands 12
## 354 2020-06-22 Midlands 12
## 355 2020-06-23 Midlands 14
## 356 2020-06-24 Midlands 13
## 357 2020-06-25 Midlands 14
## 358 2020-06-26 Midlands 3
## 359 2020-06-27 Midlands 1
## 360 2020-06-28 Midlands 0
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## 362 2020-03-02 North East and Yorkshire 0
## 363 2020-03-03 North East and Yorkshire 0
## 364 2020-03-04 North East and Yorkshire 0
## 365 2020-03-05 North East and Yorkshire 0
## 366 2020-03-06 North East and Yorkshire 0
## 367 2020-03-07 North East and Yorkshire 0
## 368 2020-03-08 North East and Yorkshire 0
## 369 2020-03-09 North East and Yorkshire 0
## 370 2020-03-10 North East and Yorkshire 0
## 371 2020-03-11 North East and Yorkshire 0
## 372 2020-03-12 North East and Yorkshire 0
## 373 2020-03-13 North East and Yorkshire 0
## 374 2020-03-14 North East and Yorkshire 0
## 375 2020-03-15 North East and Yorkshire 2
## 376 2020-03-16 North East and Yorkshire 3
## 377 2020-03-17 North East and Yorkshire 1
## 378 2020-03-18 North East and Yorkshire 2
## 379 2020-03-19 North East and Yorkshire 6
## 380 2020-03-20 North East and Yorkshire 5
## 381 2020-03-21 North East and Yorkshire 6
## 382 2020-03-22 North East and Yorkshire 7
## 383 2020-03-23 North East and Yorkshire 9
## 384 2020-03-24 North East and Yorkshire 8
## 385 2020-03-25 North East and Yorkshire 18
## 386 2020-03-26 North East and Yorkshire 21
## 387 2020-03-27 North East and Yorkshire 28
## 388 2020-03-28 North East and Yorkshire 35
## 389 2020-03-29 North East and Yorkshire 38
## 390 2020-03-30 North East and Yorkshire 64
## 391 2020-03-31 North East and Yorkshire 60
## 392 2020-04-01 North East and Yorkshire 67
## 393 2020-04-02 North East and Yorkshire 74
## 394 2020-04-03 North East and Yorkshire 100
## 395 2020-04-04 North East and Yorkshire 105
## 396 2020-04-05 North East and Yorkshire 92
## 397 2020-04-06 North East and Yorkshire 96
## 398 2020-04-07 North East and Yorkshire 102
## 399 2020-04-08 North East and Yorkshire 107
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## 401 2020-04-10 North East and Yorkshire 117
## 402 2020-04-11 North East and Yorkshire 98
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## 404 2020-04-13 North East and Yorkshire 94
## 405 2020-04-14 North East and Yorkshire 107
## 406 2020-04-15 North East and Yorkshire 96
## 407 2020-04-16 North East and Yorkshire 103
## 408 2020-04-17 North East and Yorkshire 88
## 409 2020-04-18 North East and Yorkshire 95
## 410 2020-04-19 North East and Yorkshire 88
## 411 2020-04-20 North East and Yorkshire 100
## 412 2020-04-21 North East and Yorkshire 76
## 413 2020-04-22 North East and Yorkshire 84
## 414 2020-04-23 North East and Yorkshire 63
## 415 2020-04-24 North East and Yorkshire 72
## 416 2020-04-25 North East and Yorkshire 69
## 417 2020-04-26 North East and Yorkshire 65
## 418 2020-04-27 North East and Yorkshire 65
## 419 2020-04-28 North East and Yorkshire 57
## 420 2020-04-29 North East and Yorkshire 69
## 421 2020-04-30 North East and Yorkshire 57
## 422 2020-05-01 North East and Yorkshire 64
## 423 2020-05-02 North East and Yorkshire 48
## 424 2020-05-03 North East and Yorkshire 40
## 425 2020-05-04 North East and Yorkshire 49
## 426 2020-05-05 North East and Yorkshire 40
## 427 2020-05-06 North East and Yorkshire 51
## 428 2020-05-07 North East and Yorkshire 45
## 429 2020-05-08 North East and Yorkshire 42
## 430 2020-05-09 North East and Yorkshire 44
## 431 2020-05-10 North East and Yorkshire 40
## 432 2020-05-11 North East and Yorkshire 29
## 433 2020-05-12 North East and Yorkshire 27
## 434 2020-05-13 North East and Yorkshire 28
## 435 2020-05-14 North East and Yorkshire 31
## 436 2020-05-15 North East and Yorkshire 32
## 437 2020-05-16 North East and Yorkshire 35
## 438 2020-05-17 North East and Yorkshire 26
## 439 2020-05-18 North East and Yorkshire 30
## 440 2020-05-19 North East and Yorkshire 27
## 441 2020-05-20 North East and Yorkshire 22
## 442 2020-05-21 North East and Yorkshire 33
## 443 2020-05-22 North East and Yorkshire 22
## 444 2020-05-23 North East and Yorkshire 18
## 445 2020-05-24 North East and Yorkshire 26
## 446 2020-05-25 North East and Yorkshire 21
## 447 2020-05-26 North East and Yorkshire 21
## 448 2020-05-27 North East and Yorkshire 22
## 449 2020-05-28 North East and Yorkshire 21
## 450 2020-05-29 North East and Yorkshire 25
## 451 2020-05-30 North East and Yorkshire 20
## 452 2020-05-31 North East and Yorkshire 20
## 453 2020-06-01 North East and Yorkshire 17
## 454 2020-06-02 North East and Yorkshire 23
## 455 2020-06-03 North East and Yorkshire 23
## 456 2020-06-04 North East and Yorkshire 17
## 457 2020-06-05 North East and Yorkshire 18
## 458 2020-06-06 North East and Yorkshire 21
## 459 2020-06-07 North East and Yorkshire 14
## 460 2020-06-08 North East and Yorkshire 11
## 461 2020-06-09 North East and Yorkshire 12
## 462 2020-06-10 North East and Yorkshire 18
## 463 2020-06-11 North East and Yorkshire 7
## 464 2020-06-12 North East and Yorkshire 9
## 465 2020-06-13 North East and Yorkshire 10
## 466 2020-06-14 North East and Yorkshire 11
## 467 2020-06-15 North East and Yorkshire 9
## 468 2020-06-16 North East and Yorkshire 10
## 469 2020-06-17 North East and Yorkshire 9
## 470 2020-06-18 North East and Yorkshire 10
## 471 2020-06-19 North East and Yorkshire 6
## 472 2020-06-20 North East and Yorkshire 4
## 473 2020-06-21 North East and Yorkshire 4
## 474 2020-06-22 North East and Yorkshire 6
## 475 2020-06-23 North East and Yorkshire 7
## 476 2020-06-24 North East and Yorkshire 8
## 477 2020-06-25 North East and Yorkshire 3
## 478 2020-06-26 North East and Yorkshire 6
## 479 2020-06-27 North East and Yorkshire 2
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## 528 2020-04-17 North West 98
## 529 2020-04-18 North West 113
## 530 2020-04-19 North West 71
## 531 2020-04-20 North West 83
## 532 2020-04-21 North West 76
## 533 2020-04-22 North West 86
## 534 2020-04-23 North West 85
## 535 2020-04-24 North West 66
## 536 2020-04-25 North West 65
## 537 2020-04-26 North West 55
## 538 2020-04-27 North West 54
## 539 2020-04-28 North West 57
## 540 2020-04-29 North West 62
## 541 2020-04-30 North West 59
## 542 2020-05-01 North West 45
## 543 2020-05-02 North West 56
## 544 2020-05-03 North West 55
## 545 2020-05-04 North West 48
## 546 2020-05-05 North West 48
## 547 2020-05-06 North West 44
## 548 2020-05-07 North West 49
## 549 2020-05-08 North West 42
## 550 2020-05-09 North West 30
## 551 2020-05-10 North West 41
## 552 2020-05-11 North West 35
## 553 2020-05-12 North West 38
## 554 2020-05-13 North West 25
## 555 2020-05-14 North West 26
## 556 2020-05-15 North West 33
## 557 2020-05-16 North West 32
## 558 2020-05-17 North West 24
## 559 2020-05-18 North West 31
## 560 2020-05-19 North West 35
## 561 2020-05-20 North West 27
## 562 2020-05-21 North West 27
## 563 2020-05-22 North West 26
## 564 2020-05-23 North West 31
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## 566 2020-05-25 North West 31
## 567 2020-05-26 North West 27
## 568 2020-05-27 North West 27
## 569 2020-05-28 North West 28
## 570 2020-05-29 North West 20
## 571 2020-05-30 North West 19
## 572 2020-05-31 North West 13
## 573 2020-06-01 North West 12
## 574 2020-06-02 North West 27
## 575 2020-06-03 North West 22
## 576 2020-06-04 North West 22
## 577 2020-06-05 North West 16
## 578 2020-06-06 North West 26
## 579 2020-06-07 North West 20
## 580 2020-06-08 North West 22
## 581 2020-06-09 North West 16
## 582 2020-06-10 North West 16
## 583 2020-06-11 North West 16
## 584 2020-06-12 North West 11
## 585 2020-06-13 North West 9
## 586 2020-06-14 North West 15
## 587 2020-06-15 North West 15
## 588 2020-06-16 North West 13
## 589 2020-06-17 North West 10
## 590 2020-06-18 North West 13
## 591 2020-06-19 North West 7
## 592 2020-06-20 North West 11
## 593 2020-06-21 North West 6
## 594 2020-06-22 North West 10
## 595 2020-06-23 North West 13
## 596 2020-06-24 North West 13
## 597 2020-06-25 North West 11
## 598 2020-06-26 North West 2
## 599 2020-06-27 North West 1
## 600 2020-06-28 North West 1
## 601 2020-03-01 South East 0
## 602 2020-03-02 South East 0
## 603 2020-03-03 South East 1
## 604 2020-03-04 South East 0
## 605 2020-03-05 South East 1
## 606 2020-03-06 South East 0
## 607 2020-03-07 South East 0
## 608 2020-03-08 South East 1
## 609 2020-03-09 South East 1
## 610 2020-03-10 South East 1
## 611 2020-03-11 South East 1
## 612 2020-03-12 South East 0
## 613 2020-03-13 South East 1
## 614 2020-03-14 South East 1
## 615 2020-03-15 South East 5
## 616 2020-03-16 South East 8
## 617 2020-03-17 South East 7
## 618 2020-03-18 South East 10
## 619 2020-03-19 South East 9
## 620 2020-03-20 South East 13
## 621 2020-03-21 South East 7
## 622 2020-03-22 South East 25
## 623 2020-03-23 South East 20
## 624 2020-03-24 South East 22
## 625 2020-03-25 South East 29
## 626 2020-03-26 South East 35
## 627 2020-03-27 South East 34
## 628 2020-03-28 South East 36
## 629 2020-03-29 South East 55
## 630 2020-03-30 South East 58
## 631 2020-03-31 South East 65
## 632 2020-04-01 South East 66
## 633 2020-04-02 South East 55
## 634 2020-04-03 South East 72
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## 636 2020-04-05 South East 82
## 637 2020-04-06 South East 88
## 638 2020-04-07 South East 100
## 639 2020-04-08 South East 83
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## 641 2020-04-10 South East 88
## 642 2020-04-11 South East 88
## 643 2020-04-12 South East 88
## 644 2020-04-13 South East 84
## 645 2020-04-14 South East 65
## 646 2020-04-15 South East 72
## 647 2020-04-16 South East 56
## 648 2020-04-17 South East 86
## 649 2020-04-18 South East 57
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## 651 2020-04-20 South East 87
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## 653 2020-04-22 South East 54
## 654 2020-04-23 South East 57
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## 656 2020-04-25 South East 51
## 657 2020-04-26 South East 51
## 658 2020-04-27 South East 40
## 659 2020-04-28 South East 40
## 660 2020-04-29 South East 47
## 661 2020-04-30 South East 29
## 662 2020-05-01 South East 37
## 663 2020-05-02 South East 36
## 664 2020-05-03 South East 17
## 665 2020-05-04 South East 35
## 666 2020-05-05 South East 29
## 667 2020-05-06 South East 25
## 668 2020-05-07 South East 27
## 669 2020-05-08 South East 26
## 670 2020-05-09 South East 28
## 671 2020-05-10 South East 19
## 672 2020-05-11 South East 25
## 673 2020-05-12 South East 27
## 674 2020-05-13 South East 18
## 675 2020-05-14 South East 32
## 676 2020-05-15 South East 24
## 677 2020-05-16 South East 22
## 678 2020-05-17 South East 18
## 679 2020-05-18 South East 22
## 680 2020-05-19 South East 12
## 681 2020-05-20 South East 22
## 682 2020-05-21 South East 15
## 683 2020-05-22 South East 17
## 684 2020-05-23 South East 21
## 685 2020-05-24 South East 17
## 686 2020-05-25 South East 13
## 687 2020-05-26 South East 19
## 688 2020-05-27 South East 18
## 689 2020-05-28 South East 12
## 690 2020-05-29 South East 21
## 691 2020-05-30 South East 8
## 692 2020-05-31 South East 12
## 693 2020-06-01 South East 11
## 694 2020-06-02 South East 13
## 695 2020-06-03 South East 17
## 696 2020-06-04 South East 11
## 697 2020-06-05 South East 11
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## 699 2020-06-07 South East 12
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## 701 2020-06-09 South East 10
## 702 2020-06-10 South East 11
## 703 2020-06-11 South East 5
## 704 2020-06-12 South East 6
## 705 2020-06-13 South East 6
## 706 2020-06-14 South East 7
## 707 2020-06-15 South East 7
## 708 2020-06-16 South East 11
## 709 2020-06-17 South East 8
## 710 2020-06-18 South East 4
## 711 2020-06-19 South East 6
## 712 2020-06-20 South East 5
## 713 2020-06-21 South East 3
## 714 2020-06-22 South East 2
## 715 2020-06-23 South East 8
## 716 2020-06-24 South East 6
## 717 2020-06-25 South East 4
## 718 2020-06-26 South East 3
## 719 2020-06-27 South East 4
## 720 2020-06-28 South East 3
## 721 2020-03-01 South West 0
## 722 2020-03-02 South West 0
## 723 2020-03-03 South West 0
## 724 2020-03-04 South West 0
## 725 2020-03-05 South West 0
## 726 2020-03-06 South West 0
## 727 2020-03-07 South West 0
## 728 2020-03-08 South West 0
## 729 2020-03-09 South West 0
## 730 2020-03-10 South West 0
## 731 2020-03-11 South West 1
## 732 2020-03-12 South West 0
## 733 2020-03-13 South West 0
## 734 2020-03-14 South West 1
## 735 2020-03-15 South West 0
## 736 2020-03-16 South West 0
## 737 2020-03-17 South West 2
## 738 2020-03-18 South West 2
## 739 2020-03-19 South West 4
## 740 2020-03-20 South West 3
## 741 2020-03-21 South West 6
## 742 2020-03-22 South West 7
## 743 2020-03-23 South West 8
## 744 2020-03-24 South West 7
## 745 2020-03-25 South West 9
## 746 2020-03-26 South West 11
## 747 2020-03-27 South West 13
## 748 2020-03-28 South West 21
## 749 2020-03-29 South West 18
## 750 2020-03-30 South West 23
## 751 2020-03-31 South West 23
## 752 2020-04-01 South West 22
## 753 2020-04-02 South West 23
## 754 2020-04-03 South West 30
## 755 2020-04-04 South West 42
## 756 2020-04-05 South West 32
## 757 2020-04-06 South West 34
## 758 2020-04-07 South West 39
## 759 2020-04-08 South West 47
## 760 2020-04-09 South West 24
## 761 2020-04-10 South West 46
## 762 2020-04-11 South West 43
## 763 2020-04-12 South West 23
## 764 2020-04-13 South West 27
## 765 2020-04-14 South West 24
## 766 2020-04-15 South West 32
## 767 2020-04-16 South West 29
## 768 2020-04-17 South West 33
## 769 2020-04-18 South West 25
## 770 2020-04-19 South West 31
## 771 2020-04-20 South West 26
## 772 2020-04-21 South West 26
## 773 2020-04-22 South West 23
## 774 2020-04-23 South West 17
## 775 2020-04-24 South West 19
## 776 2020-04-25 South West 15
## 777 2020-04-26 South West 27
## 778 2020-04-27 South West 13
## 779 2020-04-28 South West 17
## 780 2020-04-29 South West 15
## 781 2020-04-30 South West 26
## 782 2020-05-01 South West 6
## 783 2020-05-02 South West 7
## 784 2020-05-03 South West 10
## 785 2020-05-04 South West 17
## 786 2020-05-05 South West 14
## 787 2020-05-06 South West 19
## 788 2020-05-07 South West 16
## 789 2020-05-08 South West 6
## 790 2020-05-09 South West 11
## 791 2020-05-10 South West 5
## 792 2020-05-11 South West 8
## 793 2020-05-12 South West 7
## 794 2020-05-13 South West 7
## 795 2020-05-14 South West 6
## 796 2020-05-15 South West 4
## 797 2020-05-16 South West 4
## 798 2020-05-17 South West 6
## 799 2020-05-18 South West 4
## 800 2020-05-19 South West 6
## 801 2020-05-20 South West 1
## 802 2020-05-21 South West 9
## 803 2020-05-22 South West 6
## 804 2020-05-23 South West 6
## 805 2020-05-24 South West 3
## 806 2020-05-25 South West 8
## 807 2020-05-26 South West 11
## 808 2020-05-27 South West 5
## 809 2020-05-28 South West 10
## 810 2020-05-29 South West 7
## 811 2020-05-30 South West 3
## 812 2020-05-31 South West 2
## 813 2020-06-01 South West 7
## 814 2020-06-02 South West 2
## 815 2020-06-03 South West 7
## 816 2020-06-04 South West 2
## 817 2020-06-05 South West 2
## 818 2020-06-06 South West 1
## 819 2020-06-07 South West 3
## 820 2020-06-08 South West 3
## 821 2020-06-09 South West 0
## 822 2020-06-10 South West 1
## 823 2020-06-11 South West 2
## 824 2020-06-12 South West 2
## 825 2020-06-13 South West 2
## 826 2020-06-14 South West 0
## 827 2020-06-15 South West 1
## 828 2020-06-16 South West 2
## 829 2020-06-17 South West 0
## 830 2020-06-18 South West 0
## 831 2020-06-19 South West 0
## 832 2020-06-20 South West 2
## 833 2020-06-21 South West 0
## 834 2020-06-22 South West 1
## 835 2020-06-23 South West 1
## 836 2020-06-24 South West 1
## 837 2020-06-25 South West 0
## 838 2020-06-26 South West 3
## 839 2020-06-27 South West 0
## 840 2020-06-28 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Monday 29 Jun 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -10.7556 -3.0649 -0.3555 3.6837 6.1641
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.822e+00 5.545e-02 86.97 <2e-16 ***
## note_lag 1.266e-05 5.672e-07 22.32 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 14.64047)
##
## Null deviance: 7769.26 on 58 degrees of freedom
## Residual deviance: 862.83 on 57 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 124.233941 1.000013
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 111.290553 138.314860
## note_lag 1.000012 1.000014
Rsq(lag_mod)
## [1] 0.8889433
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.4.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.14
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.5.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 selectr_0.4-2 ggsignif_0.6.0 ellipsis_0.3.1
## [5] rprojroot_1.3-2 snakecase_0.11.0 fs_1.4.1 rstudioapi_0.11
## [9] farver_2.0.3 fansi_0.4.1 splines_4.0.2 knitr_1.29
## [13] jsonlite_1.7.0 broom_0.5.6 dbplyr_1.4.4 compiler_4.0.2
## [17] httr_1.4.1 backports_1.1.8 assertthat_0.2.1 Matrix_1.2-18
## [21] cli_2.0.2 htmltools_0.5.0 prettyunits_1.1.1 tools_4.0.2
## [25] gtable_0.3.0 glue_1.4.1 Rcpp_1.0.4.6 carData_3.0-4
## [29] cellranger_1.1.0 vctrs_0.3.1 nlme_3.1-148 matchmaker_0.1.1
## [33] crosstalk_1.1.0.1 xfun_0.15 ps_1.3.3 openxlsx_4.1.5
## [37] lifecycle_0.2.0 rstatix_0.6.0 MASS_7.3-51.6 scales_1.1.1
## [41] hms_0.5.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [45] gridExtra_2.3 stringi_1.4.6 kyotil_2019.11-22 boot_1.3-25
## [49] pkgbuild_1.0.8 zip_2.0.4 rlang_0.4.6 pkgconfig_2.0.3
## [53] evaluate_0.14 lattice_0.20-41 labeling_0.3 htmlwidgets_1.5.1
## [57] cowplot_1.0.0 processx_3.4.2 tidyselect_1.1.0 plyr_1.8.6
## [61] magrittr_1.5 R6_2.4.1 generics_0.0.2 DBI_1.1.0
## [65] pillar_1.4.4 haven_2.3.1 foreign_0.8-80 withr_2.2.0
## [69] mgcv_1.8-31 survival_3.1-12 abind_1.4-5 modelr_0.1.8
## [73] crayon_1.3.4 car_3.0-8 utf8_1.1.4 rmarkdown_2.3
## [77] viridis_0.5.1 grid_4.0.2 readxl_1.3.1 data.table_1.12.8
## [81] blob_1.2.1 callr_3.4.3 reprex_0.3.0 digest_0.6.25
## [85] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0